Fingerprinting IIoT Devices Through Machine Learning Techniques

Zhou, Feng; Qu, Hua; Liu, Hailong; Liu, Hong; Li, Bo

doi:10.1007/s11265-021-01656-0

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…[85], [150], [102], [126], [158] Vulnerability Classi cation [112], [131], [76], [117] Vulnerability Impact Analysis [199], [66], [99] Vulnerability Analytics [139], [9], [41], [77], [128] Risk Assessment Comprehensive Risk Assessment Framework [23], [48], [95], [175], [5], [22], [75], [62], [179], [122] Quantitative Risk Analysis [176], [65], [192], [7], [196], [195], [89], [189] Risk Classi cation and Prioritization [143], [194], [8] Automated and Dynamic Risk Analysis…”

Section: Predictive Vulnerability Modelingmentioning

confidence: 99%

“…A novel methodology for device-type ngerprinting in IIoT environments is introduced in Article [199],…”

Section: Vulnerability Impact Analysismentioning

confidence: 99%

“…Web Application [72], [71], [45], [41], [78], [126] Mobile Applications [42], [118], [54], [192], [39], [89], [8], [122], [177], [137], [174] IoT System [21], [80], [38], [160], [133], [199], [167], [148], [111], [191] Cyber-Physical System [76], [95], [94], [87], [77] Power and Connected and Autonomous Vehicles…”

Section: Application Domain Selected Articlementioning

confidence: 99%

“…[80], [133], [199], [167]). These predictive models help in pre-empting potential attacks and facilitating timely responses to emerging threats, thereby enhancing the overall resilience of IoT systems.…”

Section: Iot Systemsmentioning

confidence: 99%

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Artificial intelligence for system security assurance: A systematic literature review

Wen,

Shukla,

Katt

2024

Preprint

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System Security Assurance (SSA) has emerged as a critical methodology for organizations to verify the trustworthiness of their systems by evaluating security measures against industry standards, legal requirements, and best practices to identify any weakness and demonstrate compliance. In recent years, the role of Artificial Intelligence (AI) in enhancing cybersecurity has received increased attention, with an increasing number of literature reviews highlighting its diverse applications. However, there remains a significant gap in comprehensive reviews that specifically address the integration of AI within SSA frameworks. This systematic literature review seeks to fill this research gap by assessing the current state of AI in SSA, identifying key areas where AI contributes to improve SSA processes, highlighting the limitations of current methodologies, and providing the guidance for future advancements in the field of AI-driven SSA.

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